Risk-Based Domino Effect Analysis for Fire and Explosion Accidents Considering Uncertainty in Processing Facilities

Ji, Jie; Tong, Qi; Khan, Faisal; Dadashzadeh, Mohammad

doi:10.1021/acs.iecr.8b00103

Cited by 47 publications

(18 citation statements)

References 54 publications

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“…Among these methods, equipment safety management and personnel safety training are more important and useful. This also shows that passive security measures (safety management of equipment) are more robust than active security measures (emergency response) [46]. Hamza also proposed in the literature, to improve the safety of the process, the training focus on human factors is important [47], but an effective emergency response can minimize casualties and property loss.…”

Section: Discussionmentioning

confidence: 90%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

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Many chemical plant explosion accidents occur along with the development of the chemical industry. Meanwhile, the interaction and influence of various factors significantly increase the uncertainty of the evolution process of such accidents. This paper presents a framework to dynamically evaluate chemical plant explosion accidents. We propose twelve nodes to represent accident evolution and establish a Bayesian network model for chemical plant explosion accidents, combining historical data with expert experience to support the prevention, management, and real-time warning. Hypothetical scenarios and catastrophic explosion scenarios were analyzed by setting different combinations of states for nodes. Moreover, the impacts of factors such as factory type, material form, accident equipment, the emergency response on casualty and property loss are evaluated. We find that sensitivity of property loss and casualties to factory type and ongoing work are less significant; the equipment factors result in more casualties than that from personnel factors; the impact of emergency response on the accident results is significant; equipment safety management and personnel safety training are the most important measures to prevent chemical plant explosion risks.

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Section: Discussionmentioning

confidence: 90%

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Zhu

et al. 2019

Sustainability

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“…The probability due to analytical evolution for overpressure, radiation, and fragments dispersion apply Gaussian models matched with CFD tools [93][94][95][96][97], and meteorological conditions are included using a probabilistic approach [98]. The procedures can be matched to a GIS (Geographical Information System) to take into consideration actual plant lay-outs and identify possible escalation targets [99].…”

Section: Equipment Category Treshold Correlationmentioning

confidence: 99%

Analysis and Characterization of Risk Methodologies Applied to Industrial Parks

et al. 2020

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It is important to evaluate the risks in industrial parks and their processes due to the consequences of major accidents and especially the domino effect. Scientific works present a wide possibility of models to deal with these situations. In this work, based on the information extracted from the scientific literature, six groups of risk methodologies are defined, analyzed, and characterized with methods that cover the standards, preventive, probabilistic, traditional, modern, and dynamic evaluation that are applied or could be used in industrial parks. It also tries to achieve the objective of determining which are more appropriate if the possible situations and causes that can produce an accident are taken into account, identifying and evaluating them with characteristics of simultaneity and immediacy, determining the probability of an accident occurring with sufficient advance in time to avoid it under the use of a working operational procedure. There is no definitive methodology, and it is necessary that they complement each other, but considering the proposed objective, the integrated application of traditional methodologies together with the management of safety barriers, the dynamic evaluation of risks, and the inclusion of machine learning systems could fulfill the proposed objective.

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“…By using data visualization of fire incidents, regional fire risk was generally analyzed mainly based on different time ranges and different causes in our study. However, data mining and visualization can also be carried out in other aspects such as fire scale, fire place, year, and month.…”

Section: Limitations and Further Directionsmentioning

confidence: 99%

Generalized analysis of regional fire risk using data visualization of incidents

Liu

Fan

2019

Fire and Materials

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Summary Fire has been a worldwide disaster, and it is important to analyze regional fire risk for better fire prevention. Regional fire risk can be generally analyzed by using data visualization of fire incidents. A dataset of 20 622 fire incidents in Changsha City of China from 2011 to 2017 was processed and filtered as the origin risk points. Then, a generalized risk model (GRM) was established for calculating fire risk values of anywhere in Changsha. By using JavaScript and HTML5, the generalized fire risk graphics were drawn based on mapping different fire risk values with different colors. For visualizing fire incidents, the inner and outer generalization radiuses of the origin risk point were set to be 1 (110 m) and 10 (1100 m), respectively. It was found that the high and medium fire risk areas were mainly distributed in urban areas and market towns, especially in central city. The spatial distribution characteristics of fire risk of different time ranges and different causes were significantly varied. The results can provide guidance of fire prevention for the fire department in Changsha. Other cities' regional fire risk and even other types of disaster risk can also be generally analyzed using GRM.

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Risk-Based Domino Effect Analysis for Fire and Explosion Accidents Considering Uncertainty in Processing Facilities

Cited by 47 publications

References 54 publications

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Risk Analysis of Chemical Plant Explosion Accidents Based on Bayesian Network

Analysis and Characterization of Risk Methodologies Applied to Industrial Parks

Generalized analysis of regional fire risk using data visualization of incidents

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